
import pandas as pd
import numpy as np
import seaborn as sns

import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib
plt.style.use('ggplot')
from matplotlib.pyplot import figure

%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (12,8) # Adjusts the configuration of the plots we will create

#Read the data
df = pd.read_csv(r'C:\Users\izzat\Desktop\portfolioDataAnalyst\PortfolioProjectcareerchange\Careerchange.csv')


#Looking at the data

df.head()


# let see the missing data

for col in df. columns:
    pct_missing = np.mean(df[col].isnull())
    print('{} - {}%'.format(col, pct_missing))

enrollee_id - 0.0%
city - 0.0%
city_development_index - 0.0%
gender - 0.2372502236802863%
relevent_experience - 0.0%
enrolled_university - 0.019236504622725917%
education_level - 0.021622427676707426%
major_discipline - 0.14121682075753056%
experience - 0.0041753653444676405%
company_size - 0.31106471816283926%
company_type - 0.3219504920966299%
last_new_job - 0.022368028631076647%
training_hours - 0.0%


df.dtypes

enrollee_id                 int64
city                       object
city_development_index    float64
gender                     object
relevent_experience        object
enrolled_university        object
education_level            object
major_discipline           object
experience                 object
company_size               object
company_type               object
last_new_job               object
training_hours              int64
dtype: object


# To fill the missing data

# Data cleaning

# To fill the missing data

df['gender'].fillna('other', inplace=True)

df['enrolled_university'].fillna('no_enrollment', inplace=True)

df['education_level'].fillna('graduates', inplace=True)

df['major_discipline'].fillna('No_major', inplace=True)

df['experience'].fillna('1', inplace=True)

df['company_size'].fillna( '100', inplace=True)

df['company_type'].fillna( 'Not Stated', inplace=True)

df['last_new_job'].fillna( '0', inplace=True)


df.head()


# To drop Never in Last new job

df.replace ('never',0)

df.replace('others','Other')


# checking the missing data again

for col in df. columns:
    pct_missing = np.mean(df[col].isnull())
    print('{} - {}%'.format(col, pct_missing))

enrollee_id - 0.0%
city - 0.0%
city_development_index - 0.0%
gender - 0.0%
relevent_experience - 0.0%
enrolled_university - 0.0%
education_level - 0.0%
major_discipline - 0.0%
experience - 0.0%
company_size - 0.0%
company_type - 0.0%
last_new_job - 0.0%
training_hours - 0.0%


# sort data by city

# how city development index affect career change

df = df.sort_values(by= ['city'],inplace = False, ascending = False)

pd.set_option ('display.max_rows', None)

df.head()


# Higher city development more career change

#city development high correleation

plt.bar (x= df['gender'], height=df['city_development_index'])

plt.title('number of Career change due to city development')

plt.xlabel('gender')

plt.ylabel('number of career change')

plt.show()


# Looking at correlation

df.corr(method='pearson')

#less correlation between development index and training hours


# try numerized gender, enrooled university, relevant experiences


df_numerized = df

for col_gender in df_numerized.columns:
    if(df_numerized[col_gender].dtype=='object'):
        df_numerized[col_gender]= df_numerized[col_gender].astype('category')
        df_numerized[col_gender]= df_numerized[col_gender].cat.codes
        
df_numerized


# Looking at correlation again

df.corr(method='pearson')

sns.heatmap


# Trying to look at correlation again

correlation_matrix = df.corr (method= 'pearson')

sns.heatmap(correlation_matrix , annot= True)

plt.title (' correlation Matrix vs Numeric Features')

plt.show()

plt.show()


# conclusion, there is high correlatiob between university enrollment and development index
# there is medium correlation between experience and last new job
# there is less correlation between experience and relevant experience

	enrollee_id	city	city_development_index	gender	relevent_experience	enrolled_university	education_level	major_discipline	experience	company_size	company_type	last_new_job	training_hours
0	7129	city_23	0.899	Male	No relevent experience	no_enrollment	Graduate	STEM	2	NaN	NaN	1	23
1	31037	city_44	0.725	Male	No relevent experience	Part time course	Masters	STEM	5	NaN	NaN	never	39
2	22179	city_103	0.920	Male	No relevent experience	Full time course	Graduate	STEM	7	NaN	NaN	2	262
3	29724	city_50	0.896	Male	No relevent experience	Full time course	Graduate	STEM	7	NaN	NaN	never	78
4	17977	city_149	0.689	Female	Has relevent experience	Full time course	Masters	STEM	19	50-99	Pvt Ltd	1	125

	enrollee_id	city	city_development_index	gender	relevent_experience	enrolled_university	education_level	major_discipline	experience	company_size	company_type	last_new_job	training_hours
0	7129	city_23	0.899	Male	No relevent experience	no_enrollment	Graduate	STEM	2	100	Not Stated	1	23
1	31037	city_44	0.725	Male	No relevent experience	Part time course	Masters	STEM	5	100	Not Stated	never	39
2	22179	city_103	0.920	Male	No relevent experience	Full time course	Graduate	STEM	7	100	Not Stated	2	262
3	29724	city_50	0.896	Male	No relevent experience	Full time course	Graduate	STEM	7	100	Not Stated	never	78
4	17977	city_149	0.689	Female	Has relevent experience	Full time course	Masters	STEM	19	50-99	Pvt Ltd	1	125

	enrollee_id	city	city_development_index	gender	relevent_experience	enrolled_university	education_level	major_discipline	experience	company_size	company_type	last_new_job	training_hours
1844	13022	city_99	0.915	Male	Has relevent experience	no_enrollment	Graduate	STEM	>20	100-500	Pvt Ltd	>4	36
6059	4309	city_99	0.915	Female	Has relevent experience	no_enrollment	Graduate	STEM	3	500-999	Pvt Ltd	never	62
28	25952	city_99	0.915	Male	Has relevent experience	no_enrollment	Graduate	STEM	>20	100-500	NGO	2	23
3436	540	city_99	0.915	Male	Has relevent experience	no_enrollment	Graduate	STEM	>20	<10	Pvt Ltd	>4	48
170	6454	city_99	0.915	Male	Has relevent experience	no_enrollment	Masters	STEM	12	100-500	Pvt Ltd	1	17

	enrollee_id	city_development_index	training_hours
enrollee_id	1.000000	-0.060972	-0.003842
city_development_index	-0.060972	1.000000	0.012830
training_hours	-0.003842	0.012830	1.000000

	enrollee_id	city	city_development_index	gender	relevent_experience	enrolled_university	education_level	major_discipline	experience	company_size	company_type	last_new_job	training_hours
enrollee_id	1.000000	-0.010087	-0.060972	-0.052587	0.043517	-0.042809	-0.034351	-0.021492	-0.011071	-0.017383	-0.027229	-0.026089	-0.003842
city	-0.010087	1.000000	-0.334426	0.055588	-0.001456	-0.058496	0.000612	0.097323	-0.043460	0.020762	-0.007487	-0.002567	0.000772
city_development_index	-0.060972	-0.334426	1.000000	-0.164521	-0.063996	0.175051	0.045091	-0.116416	0.042462	0.006708	0.024751	0.009551	0.012830
gender	-0.052587	0.055588	-0.164521	1.000000	0.094539	-0.087880	0.057793	-0.006621	-0.010809	-0.024260	-0.027499	-0.016389	0.006046
relevent_experience	0.043517	-0.001456	-0.063996	0.094539	1.000000	-0.363622	0.103577	-0.168386	0.001447	-0.272449	-0.193112	0.169456	-0.019680
enrolled_university	-0.042809	-0.058496	0.175051	-0.087880	-0.363622	1.000000	0.075284	-0.019131	0.027785	0.134752	0.130701	0.017907	0.011632
education_level	-0.034351	0.000612	0.045091	0.057793	0.103577	0.075284	1.000000	-0.154132	-0.009650	-0.066424	-0.046549	0.100809	-0.004844
major_discipline	-0.021492	0.097323	-0.116416	-0.006621	-0.168386	-0.019131	-0.154132	1.000000	0.010482	0.084477	0.078649	-0.054993	-0.005976
experience	-0.011071	-0.043460	0.042462	-0.010809	0.001447	0.027785	-0.009650	0.010482	1.000000	-0.035181	-0.007594	0.047858	-0.015334
company_size	-0.017383	0.020762	0.006708	-0.024260	-0.272449	0.134752	-0.066424	0.084477	-0.035181	1.000000	0.164233	-0.145250	0.016952
company_type	-0.027229	-0.007487	0.024751	-0.027499	-0.193112	0.130701	-0.046549	0.078649	-0.007594	0.164233	1.000000	0.026674	-0.008099
last_new_job	-0.026089	-0.002567	0.009551	-0.016389	0.169456	0.017907	0.100809	-0.054993	0.047858	-0.145250	0.026674	1.000000	-0.007013
training_hours	-0.003842	0.000772	0.012830	0.006046	-0.019680	0.011632	-0.004844	-0.005976	-0.015334	0.016952	-0.008099	-0.007013	1.000000

	enrollee_id	city	city_development_index	gender	relevent_experience	enrolled_university	education_level	major_discipline	experience	company_size	company_type	last_new_job	training_hours
1844	13022	117	0.915	1	0	2	0	6	21	1	6	5	36
6059	4309	117	0.915	0	0	2	0	6	13	5	6	6	62
28	25952	117	0.915	1	0	2	0	6	21	1	2	2	23
3436	540	117	0.915	1	0	2	0	6	21	7	6	5	48
170	6454	117	0.915	1	0	2	2	6	3	1	6	1	17
5378	24824	117	0.915	0	1	2	3	2	1	2	2	2	41
2874	10691	117	0.915	0	1	0	0	1	0	0	3	1	40
2218	20271	117	0.915	3	1	0	1	4	15	8	6	1	72
976	21190	117	0.915	3	0	2	0	6	3	2	6	0	20
5804	17863	117	0.915	1	0	2	0	6	21	0	3	5	94
1788	4172	117	0.915	3	0	2	0	6	15	8	3	1	22
3395	1676	117	0.915	3	0	2	2	6	21	4	6	2	39
1796	21157	117	0.915	1	1	2	1	4	18	6	5	1	42
2624	6326	117	0.915	1	0	2	0	6	18	2	6	2	26
6241	1725	117	0.915	1	0	2	2	2	7	0	3	4	60
3492	935	117	0.915	3	0	0	0	6	15	8	3	2	42
6535	27057	117	0.915	1	0	2	0	6	19	1	6	5	112
5282	29684	117	0.915	3	0	2	0	6	11	6	6	1	30
5681	7709	117	0.915	1	0	2	0	6	1	7	1	1	58
1382	29485	117	0.915	1	1	0	0	6	14	0	3	1	48
5960	30600	117	0.915	1	0	2	1	4	11	0	3	1	39
211	25856	117	0.915	1	0	2	0	6	16	7	6	2	188
3963	1568	117	0.915	1	0	2	0	6	6	4	6	4	50
4458	26737	117	0.915	1	1	2	0	6	21	4	6	5	50
3093	21197	117	0.915	1	0	2	0	6	17	6	6	5	70
5318	19306	117	0.915	0	0	2	0	6	9	4	3	2	28
5555	10121	117	0.915	3	0	2	2	6	8	4	6	1	6
3958	31348	117	0.915	2	0	2	3	6	0	2	6	5	35
3873	21111	117	0.915	0	0	2	0	6	21	4	6	5	37
3542	25813	117	0.915	1	1	2	0	6	11	1	5	1	31
2504	16322	117	0.915	1	1	2	0	1	17	4	6	1	44
46	12291	117	0.915	1	0	2	0	6	5	0	3	2	52
4338	789	116	0.949	3	1	2	0	6	21	7	6	1	55
1087	28850	116	0.949	1	0	2	0	6	18	4	6	5	77
960	6754	116	0.949	1	1	2	3	2	16	2	5	5	127
2225	20416	116	0.949	1	0	2	1	4	12	0	3	6	149
2875	11246	116	0.949	3	0	1	2	2	9	1	6	1	306
1540	17459	116	0.949	1	0	2	2	6	21	0	3	5	48
2883	7721	116	0.949	3	0	2	0	6	21	7	1	2	35
2067	726	116	0.949	3	0	2	2	6	4	8	6	4	35